The Efficacy of Platelet-Rich Plasma in the Field of Hair Restoration and Facial Aesthetics—A Systematic Review and Meta-analysis
Bibliographic record
Abstract
As a promising alternative to traditional treatment, platelet-rich plasma (PRP) is being used to encourage hair growth through the release of growth factors and cytokines. In addition to hair restoration, PRP’s multifactorial capabilities can also be used to treat aging skin, facial scarring, and acne. The purpose of this review is to critically examine the success of PRP in the field of dermatology, with specific attention to the role of PRP in hair restoration. Where possible, meta-analyses were used to evaluate the efficacy of PRP. In androgenetic alopecia (AGA) patients, 3 monthly PRP injections (1 session administered every month for 3 months) exhibited greater efficacy over placebo as measured by change in total hair density (hair/cm 2 ) over the treatment period (mean difference: 25.61, 95% CI: 4.45 to 46.77; P = .02). The studies included in the meta-analysis used a half-head design, which may have influenced the results because of the effects PRP can induce. Controlled studies suggest that 2 to 4 sessions of PRP combined with traditional therapies and procedures can help minimize acne scarring and facial burns, improve aesthetic results, and decrease recovery time. However, data for these indications are lacking and are less robust in design. In conclusion, to achieve an improvement in hair restoration in patients with mild AGA, 3 initial monthly PRP injections should be given. Only upon completion of rigorous, randomized, controlled studies can standardized and effective PRP protocols for treating dermatology conditions such as acne scarring, facial burns, and aging skin be determined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.023 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".